12_geospatial_06_projection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projections\n",
    "\n",
    "This script helps to practice the projection to 2D. Broadly speaking, people use two systems in spatial projection: lat/lon and meters. More specifically, the coordinate systems are adapted for different local areas. Researchers need to use the projection consistently, and choose the best projection for their own purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                            Version\r\n",
      "---------------------------------- -------------------\r\n",
      "absl-py                            0.9.0\r\n",
      "access                             1.1.2\r\n",
      "affine                             2.3.0\r\n",
      "alabaster                          0.7.12\r\n",
      "anaconda-client                    1.7.2\r\n",
      "anaconda-navigator                 1.9.7\r\n",
      "anaconda-project                   0.8.3\r\n",
      "arrow                              0.15.7\r\n",
      "arviz                              0.6.1\r\n",
      "asn1crypto                         1.3.0\r\n",
      "astor                              0.8.1\r\n",
      "astroid                            2.3.3\r\n",
      "astropy                            4.0.1.post1\r\n",
      "atomicwrites                       1.4.0\r\n",
      "attrs                              19.3.0\r\n",
      "Babel                              2.8.0\r\n",
      "backcall                           0.2.0\r\n",
      "backports.functools-lru-cache      1.6.1\r\n",
      "backports.os                       0.1.1\r\n",
      "backports.shutil-get-terminal-size 1.0.0\r\n",
      "backports.tempfile                 1.0\r\n",
      "backports.weakref                  1.0.post1\r\n",
      "beautifulsoup4                     4.9.1\r\n",
      "biogeme                            3.2.6\r\n",
      "bitarray                           1.2.2\r\n",
      "bkcharts                           0.2\r\n",
      "bleach                             3.1.5\r\n",
      "bokeh                              2.1.1\r\n",
      "boto                               2.49.0\r\n",
      "Bottleneck                         1.3.2\r\n",
      "branca                             0.4.1\r\n",
      "brotlipy                           0.7.0\r\n",
      "Cartopy                            0.18.0\r\n",
      "certifi                            2020.6.20\r\n",
      "cffi                               1.14.0\r\n",
      "cftime                             1.1.0\r\n",
      "chardet                            3.0.4\r\n",
      "click                              7.1.2\r\n",
      "click-plugins                      1.1.1\r\n",
      "cligj                              0.5.0\r\n",
      "cloudpickle                        1.4.1\r\n",
      "clyent                             1.2.2\r\n",
      "colorama                           0.4.3\r\n",
      "conda                              4.9.2\r\n",
      "conda-build                        3.18.9\r\n",
      "conda-package-handling             1.7.0\r\n",
      "conda-verify                       3.4.2\r\n",
      "configparser                       5.0.0\r\n",
      "contextily                         1.0.0\r\n",
      "contextlib2                        0.6.0.post1\r\n",
      "coverage                           5.3\r\n",
      "cryptography                       2.9.2\r\n",
      "cycler                             0.10.0\r\n",
      "Cython                             0.29.20\r\n",
      "cytoolz                            0.10.1\r\n",
      "dask                               2.19.0\r\n",
      "decorator                          4.4.2\r\n",
      "defusedxml                         0.6.0\r\n",
      "descartes                          1.1.0\r\n",
      "distributed                        2.19.0\r\n",
      "docker-pycreds                     0.4.0\r\n",
      "docutils                           0.16\r\n",
      "dropbox                            10.2.0\r\n",
      "entrypoints                        0.3\r\n",
      "esda                               2.3.1\r\n",
      "et-xmlfile                         1.0.1\r\n",
      "factor-analyzer                    0.3.2\r\n",
      "fastcache                          1.1.0\r\n",
      "filelock                           3.0.12\r\n",
      "Fiona                              1.8.13.post1\r\n",
      "Flask                              1.1.2\r\n",
      "folium                             0.11.0\r\n",
      "fsspec                             0.7.4\r\n",
      "future                             0.18.2\r\n",
      "gast                               0.2.2\r\n",
      "GDAL                               2.4.2\r\n",
      "geographiclib                      1.50\r\n",
      "geopandas                          0.8.1\r\n",
      "geoplot                            0.4.1\r\n",
      "geopy                              2.0.0\r\n",
      "geos                               0.2.2\r\n",
      "gevent                             20.6.2\r\n",
      "giddy                              2.3.3\r\n",
      "gitdb                              4.0.4\r\n",
      "GitPython                          3.1.1\r\n",
      "glob2                              0.7\r\n",
      "gmpy2                              2.0.8\r\n",
      "google-pasta                       0.1.8\r\n",
      "GPUtil                             1.4.0\r\n",
      "gql                                0.2.0\r\n",
      "graphql-core                       1.1\r\n",
      "greenlet                           0.4.16\r\n",
      "grpcio                             1.27.1\r\n",
      "h5py                               2.10.0\r\n",
      "HeapDict                           1.0.1\r\n",
      "html5lib                           1.1\r\n",
      "hypothesis                         5.18.3\r\n",
      "idna                               2.10\r\n",
      "imageio                            2.8.0\r\n",
      "imagesize                          1.2.0\r\n",
      "importlib-metadata                 1.7.0\r\n",
      "inequality                         1.0.0\r\n",
      "ipykernel                          5.3.0\r\n",
      "ipython                            7.16.1\r\n",
      "ipython-genutils                   0.2.0\r\n",
      "ipywidgets                         7.5.1\r\n",
      "isort                              4.3.21\r\n",
      "itsdangerous                       1.1.0\r\n",
      "jdcal                              1.4.1\r\n",
      "jedi                               0.17.1\r\n",
      "jeepney                            0.4.3\r\n",
      "Jinja2                             2.11.2\r\n",
      "joblib                             0.15.1\r\n",
      "json5                              0.9.5\r\n",
      "jsonschema                         3.2.0\r\n",
      "jupyter                            1.0.0\r\n",
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      "keyring                            21.2.1\r\n",
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      "munch                              2.5.0\r\n",
      "munkres                            1.1.4\r\n",
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      "nbformat                           5.0.7\r\n",
      "netCDF4                            1.5.3\r\n",
      "networkx                           2.5\r\n",
      "nltk                               3.5\r\n",
      "nose                               1.3.7\r\n",
      "notebook                           6.0.3\r\n",
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      "numpydoc                           1.0.0\r\n",
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      "olefile                            0.46\r\n",
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      "python-dateutil                    2.8.0\r\n",
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      "PyWavelets                         1.1.1\r\n",
      "PyYAML                             5.3.1\r\n",
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      "regex                              2020.6.8\r\n",
      "requests                           2.24.0\r\n",
      "rope                               0.17.0\r\n",
      "Rtree                              0.9.4\r\n",
      "ruamel-yaml                        0.15.87\r\n",
      "scikit-image                       0.16.2\r\n",
      "scikit-learn                       0.23.2\r\n",
      "scipy                              1.5.0\r\n",
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      "SecretStorage                      3.1.2\r\n",
      "segregation                        1.4.0\r\n",
      "Send2Trash                         1.5.0\r\n",
      "sentry-sdk                         0.14.3\r\n",
      "setuptools                         47.3.1.post20200622\r\n",
      "Shapely                            1.7.0\r\n",
      "shortuuid                          1.0.1\r\n",
      "simplegeneric                      0.8.1\r\n",
      "simplejson                         3.17.2\r\n",
      "singledispatch                     3.4.0.3\r\n",
      "six                                1.15.0\r\n",
      "smmap                              3.0.2\r\n",
      "snorkel                            0.9.6\r\n",
      "snowballstemmer                    2.0.0\r\n",
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      "sphinxcontrib-devhelp              1.0.2\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spyder                             3.3.6\r\n",
      "spyder-kernels                     0.5.2\r\n",
      "SQLAlchemy                         1.3.18\r\n",
      "statsmodels                        0.11.1\r\n",
      "subprocess32                       3.5.4\r\n",
      "sympy                              1.6\r\n",
      "tables                             3.6.1\r\n",
      "tabulate                           0.8.6\r\n",
      "tblib                              1.6.0\r\n",
      "tensorboard                        1.15.0\r\n",
      "tensorflow-estimator               1.15.1\r\n",
      "tensorflow-gpu                     1.15.0\r\n",
      "termcolor                          1.1.0\r\n",
      "terminado                          0.8.3\r\n",
      "testpath                           0.4.4\r\n",
      "Theano                             1.0.4\r\n",
      "threadpoolctl                      2.1.0\r\n",
      "tobler                             0.4.0\r\n",
      "toolz                              0.10.0\r\n",
      "torch                              1.4.0\r\n",
      "torchvision                        0.5.0\r\n",
      "tornado                            6.0.4\r\n",
      "tqdm                               4.47.0\r\n",
      "traitlets                          4.3.3\r\n",
      "typing-extensions                  3.7.4.2\r\n",
      "unicodecsv                         0.14.1\r\n",
      "Unidecode                          1.1.1\r\n",
      "urllib3                            1.24.3\r\n",
      "wandb                              0.8.34\r\n",
      "watchdog                           0.10.2\r\n",
      "wcwidth                            0.2.5\r\n",
      "webencodings                       0.5.1\r\n",
      "Werkzeug                           1.0.1\r\n",
      "wheel                              0.34.2\r\n",
      "widgetsnbextension                 3.5.1\r\n",
      "wrapt                              1.12.1\r\n",
      "wurlitzer                          2.0.0\r\n",
      "xarray                             0.15.0\r\n",
      "xlrd                               1.2.0\r\n",
      "XlsxWriter                         1.2.9\r\n",
      "xlwt                               1.3.0\r\n",
      "zict                               2.0.0\r\n",
      "zipp                               3.1.0\r\n",
      "zope.event                         4.4\r\n",
      "zope.interface                     4.7.1\r\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd \n",
    "import geoplot \n",
    "import pandas as pd\n",
    "from shapely.geometry import Point\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.read_file(\"./data_shenhao/texas/texas.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show current projection.\n",
    "# EPSG:4326; ellipsoidal coordinate with lat and long.\n",
    "# WGS84 is standard for GPS using a reference ellipsoid.\n",
    "# Check info here: https://epsg.io/4326\n",
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      POLYGON ((-100.00687 36.49388, -100.00114 36.4...\n",
       "1      POLYGON ((-102.16757 36.05463, -102.16522 36.4...\n",
       "2      POLYGON ((-102.16757 36.05463, -103.02356 36.0...\n",
       "3      POLYGON ((-101.61950 36.05471, -101.62032 36.4...\n",
       "4      POLYGON ((-101.08231 36.05626, -101.08967 36.4...\n",
       "                             ...                        \n",
       "249    MULTIPOLYGON (((-97.96375 26.61275, -97.99151 ...\n",
       "250    POLYGON ((-98.59997 26.26045, -98.67792 26.242...\n",
       "251    POLYGON ((-98.59997 26.26045, -98.34128 26.787...\n",
       "252    MULTIPOLYGON (((-97.86896 26.34835, -97.87361 ...\n",
       "253    MULTIPOLYGON (((-97.25125 26.41930, -97.23211 ...\n",
       "Name: geometry, Length: 254, dtype: geometry"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['geometry'] # lat and lon as units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vis\n",
    "ax = gdf.plot()\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# projection 1 \n",
    "gdf_proj_1 = gdf.to_crs(\"epsg:3879\")\n",
    "# gdf_proj_1 = gdf.to_crs(epsg=3879)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:3879>\n",
       "Name: ETRS89 / GK25FIN\n",
       "Axis Info [cartesian]:\n",
       "- N[north]: Northing (metre)\n",
       "- E[east]: Easting (metre)\n",
       "Area of Use:\n",
       "- name: Finland - 24.5°E to 25.5°E onshore nominal\n",
       "- bounds: (24.5, 59.94, 25.5, 68.9)\n",
       "Coordinate Operation:\n",
       "- name: Finland Gauss-Kruger zone 25\n",
       "- method: Transverse Mercator\n",
       "Datum: European Terrestrial Reference System 1989\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_proj_1.crs\n",
    "# info: https://epsg.io/3879\n",
    "# sw: projection to a small area in Finland. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vis\n",
    "ax = gdf_proj_1.plot()\n",
    "ax.set_axis_off()\n",
    "plt.show()\n",
    "# sw: Nice. The projection that fits Finland makes Texas look horrible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# projection 2\n",
    "gdf_proj_2 = gdf.to_crs(\"epsg:26916\") # For US projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:26916>\n",
       "Name: NAD83 / UTM zone 16N\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: North America - 90°W to 84°W and NAD83 by country\n",
       "- bounds: (-90.0, 23.97, -84.0, 84.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 16N\n",
       "- method: Transverse Mercator\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_proj_2.crs\n",
    "# UTM: Universal Transverse Mercator\n",
    "# Note that is uses meters for E and N (not longitude and latitude) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      POLYGON ((-667829.483 4118356.514, -667334.059...\n",
       "1      POLYGON ((-870913.945 4098278.937, -862871.991...\n",
       "2      POLYGON ((-870913.945 4098278.937, -948833.552...\n",
       "3      POLYGON ((-821051.191 4090481.827, -813566.942...\n",
       "4      POLYGON ((-772188.478 4083300.353, -765681.327...\n",
       "                             ...                        \n",
       "249    MULTIPOLYGON (((-595492.303 2990877.783, -5982...\n",
       "250    POLYGON ((-663181.962 2957100.631, -671253.014...\n",
       "251    POLYGON ((-663181.962 2957100.631, -631745.201...\n",
       "252    MULTIPOLYGON (((-588486.656 2960449.692, -5880...\n",
       "253    MULTIPOLYGON (((-525573.370 2963231.402, -5236...\n",
       "Name: geometry, Length: 254, dtype: geometry"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_proj_2['geometry'] # sw: meters as units?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vis\n",
    "ax = gdf_proj_2.plot()\n",
    "ax.set_axis_off()\n",
    "plt.show()\n",
    "# sw: small rotations exists, Hmm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}